Exploring the Association Between Inclusion Body Myositis and T Cell Large Granular Lymphocytic Leukemia: Insights from Biomarker Discovery and TCR Repertoire Analysis Using Machine Learning

Emily J McLeish

Inclusion body myositis (IBM) is a progressive muscle disease primarily affecting aging individuals, leading to muscle weakness and atrophy, particularly in the quadriceps and finger flexor muscles. Its complex pathoetiology involves aging, autoimmune mechanisms, chronic inflammation, and degenerative processes, resulting in diagnostic challenges and an absence of effective treatments. The initial objective of my PhD research project was to perform flow cytometry analysis on peripheral blood samples taken from IBM and healthy controls (HC) and utilize ML methods to identify distinct immune signatures in IBM patients, differentiating them from normal aging cellular changes. Our Random Forest model achieved a 94% AUC ROC, highlighting significant differences in immunophenotype, such as increased CD8+ T-bet+ cells and altered γδ T cell repertoire. Unsupervised ML successfully categorized IBM patients into three clusters, based on immune signatures, one of which exhibited a pro-inflammatory profile, the second was a distinct less inflammatory profile and the third showed a highly differentiated T cell profile with marked expansion of the CD8+CD57+ cell population. Excessive expansion of the cells is associated with T-cell large granular lymphocytic leukemia. Building on these findings, I explored the association between IBM and T-cell large granular lymphocytic (T-LGL) leukemia, revealing that 40% of IBM patients exhibit heightened T-LGL expansion. T-LGLHIGH patients displayed a senescent-like T cell profile with upregulated inhibitory receptors which were more pronounced within the CD8+ compartment but also extended to the CD4+, and γδ T cell populations. The investigation of HLA haplotypes revealed increased allele frequency with the HLA-C*14:02:01 allele with T-LGLHIGH individuals. Clinical assessment of patients with and without these expansions suggests that these cells play a role in the severity of IBM. The next aim of my research work was to determine the nature of these cells, discerning whether they signify a reactive or pathological expansion. This inquiry led me to investigate the clonality of the CD8+CD57+ TCRβ repertoire through high-throughput sequencing. IBM patients are predominantly polyclonal, with two oligoclonal patients identified, suggesting this expansion may be reactive in nature and challenging the previous association of IBM with TLGL leukemia. A further comparison of TCRβ repertoire analysis between blood and muscle T cells strengthened these observations, reinforcing the likelihood that these cells constitute a reactive T-LGL expansion contributing to the inflammatory milieu. Notably, shared dominant clonotypes among patients are scarce, suggesting a subject-specific rather than disease-specific expansion, except for a notable exception in two patients. Furthermore, our investigation into TCR convergence groups and alignment with known antigens, particularly from CMV and EBV, hints at a potential role of chronic viral stimulation in driving T-LGL expansions in IBM. Overall, ML models have successfully stratified IBM patients based on immunophenotype, with one cohort resembling expanded T-LGLs. Extensive immunophenotyping of these TLGLHIGH patients reveals marked T cell dysregulation across not only the CD8 compartment but also CD4, and gamma delta populations. Despite initial reports of a leukemic-like disorder, clonality analysis suggests this population is reminiscent of a chronic reactive lymphoproliferative disease in IBM that is driven by and perpetuated through chronic inflammation.

Exploring the Association Between Inclusion Body Myositis and T Cell Large Granular Lymphocytic Leukemia: Insights from Biomarker Discovery and TCR Repertoire Analysis Using Machine Learning

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